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The ANOVA feature ranking method and support vector machine classifier were used to construct 3 single-sequence models and 1 integrated model combined with the features of three sequences. All the models were established in the training set and independently verified in the internal test and external validation set. The AUC was used to compared the predictive performance of PSAD with each model. Hosmer\u2013lemeshow test was used to evaluate the degree of fitting between prediction probability and pathological results. Non-inferiority test was used to check generalization performance of the integrated model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The difference of PSAD between PCa and benign lesions was statistically significant (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.006), with the mean AUC of 0.701 for predicting clinically significant prostate cancer (internal test AUC\u2009=\u20090.709 vs. external validation AUC\u2009=\u20090.692,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.013) and 0.630 for predicting all cancer (internal test AUC\u2009=\u20090.637 vs. external validation AUC\u2009=\u20090.623,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.036). T2WI-model with the mean AUC of 0.717 for predicting csPCa (internal test AUC\u2009=\u20090.738 vs. external validation AUC\u2009=\u20090.695,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.264) and 0.634 for predicting all cancer (internal test AUC\u2009=\u20090.678 vs. external validation AUC\u2009=\u20090.589,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.547). DWI-model with the mean AUC of 0.658 for predicting csPCa (internal test AUC\u2009=\u20090.635 vs. external validation AUC\u2009=\u20090.681,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.086) and 0.655 for predicting all cancer (internal test AUC\u2009=\u20090.712 vs. external validation AUC\u2009=\u20090.598,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.437). ADC-model with the mean AUC of 0.746 for predicting csPCa (internal test AUC\u2009=\u20090.767 vs. external validation AUC\u2009=\u20090.724,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.269) and 0.645 for predicting all cancer (internal test AUC\u2009=\u20090.650 vs. external validation AUC\u2009=\u20090.640,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.848). Integrated model with the mean AUC of 0.803 for predicting csPCa (internal test AUC\u2009=\u20090.804 vs. external validation AUC\u2009=\u20090.801,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.019) and 0.778 for predicting all cancer (internal test AUC\u2009=\u20090.801 vs. external validation AUC\u2009=\u20090.754,\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.047).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The radiomics model based on machine learning has the potential to be a non-invasive tool to distinguish cancerous, noncancerous and csPCa in PI-RADS 3 lesions, and has relatively high generalization ability between different date set.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-023-01002-9","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T07:03:49Z","timestamp":1680073429000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study"],"prefix":"10.1186","volume":"23","author":[{"given":"Pengfei","family":"Jin","sequence":"first","affiliation":[]},{"given":"Junkang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Liqin","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ao","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Ximing","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"1002_CR1","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.clinimag.2021.06.024","volume":"88","author":"G Wang","year":"2022","unstructured":"Wang G, Yu G, Chen J, et al. 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